Robust Design of Multi-Energy Systems Accounting for Mixed-Integer Operational Problems

Identifying robust designs for multi-energy systems is computationally challenging.

As rigorous approaches are often computationally intractable, heuristics are employed to generate candidate designs.

Specifically, we consider a heuristic that iteratively identifies and adds extreme scenarios to the design problem.

We theoretically investigate how three common nonconvexities, i.e., piecewise-linear energy inflow-outflow relationships, minimum part-loads, and storage complementarity, affect the robustness of designs identified by this heuristic.

We find that, if surplus energy cannot be curtailed, any of these nonconvexities may cause the heuristic to fail.

If curtailment is allowed, storage complementarity does not compromise robustness, and convex piecewise-linear inflow-outflow relationships can be reformulated linearly.

However, minimum part-loads may lead to failure of the heuristic.

Furthermore, if the optimal value function of the operational problem is nonconvex in the uncertain variables, the heuristic may fail.

We consider an illustrative multi-energy system, in which minimum part-loads and nonconvex dependence of the objective function on heat-pump efficiency are identified as possible failure modes.

We propose a hybrid method that discretizes integer variables and embeds the dual of the lower-level problem into a single-level formulation to verify whether a design identified by the heuristic is robust.

The results show that the heuristic may identify robust designs despite the presence of certain nonconvexities, but the success is case-dependent.

Hence, the heuristic can serve as a first step in identifying robust designs; however, when robustness guarantees are required, rigorous methods are necessary.